Childhood Obesity Evidence Base Project: Methods for Taxonomy Development for Application in Taxonomic Meta-Analysis

Meta-analysis has been used to examine the effectiveness of childhood obesity prevention efforts, yet traditional conventional meta-analytic methods restrict the kinds of studies included, and either narrowly define mechanisms and agents of change, or examine the effectiveness of whole interventions as opposed to the specific actions that comprise interventions. Taxonomic meta-analytic methods widen the aperture of what can be included in a meta-analysis data set, allowing for inclusion of many types of interventions and study designs. The National Collaborative on Childhood Obesity Research Childhood Obesity Evidence Base (COEB) project focuses on interventions intended to prevent childhood obesity in children 2-5 years old who have an outcome measure of BMI. The COEB created taxonomies, anchored in the Social Ecological Model, which catalog specific outcomes, intervention components, intended recipients, and contexts of policies, initiatives, and interventions conducted at the individual, interpersonal, organizational, community, and societal level. Taxonomies were created by discovery from the literature itself using grounded theory. This article describes the process used for a novel taxonomic meta-analysis of childhood obesity prevention studies between the years 2010 and 2019. This method can be applied to other areas of research, including obesity prevention in additional populations.

Childhood Obesity Evidence Base Project: A Rationale for Taxonomic versus Conventional Meta-Analysis

Introduction: There is a great need for analytic techniques that allow for the synthesis of learning across seemingly idiosyncratic interventions. Objectives: The primary objective of this paper is to introduce taxonomic meta-analysis and explain how it is different from conventional meta-analysis. Results: Conventional meta-analysis has previously been used to examine the effectiveness of childhood obesity prevention interventions. However, these tend to examine narrowly defined sections of obesity prevention initiatives, and as such, do not allow the field to draw conclusions across settings, participants, or subjects. Compared with conventional meta-analysis, taxonomic meta-analysis widens the aperture of what can be examined to synthesize evidence across interventions with diverse topics, goals, research designs, and settings. A component approach is employed to examine interventions at the level of their essential features or activities to identify the concrete aspects of interventions that are used (intervention components), characteristics of the intended populations (target population or intended recipient characteristics), and facets of the environments in which they operate (contextual elements), and the relationship of these components to effect size. In addition, compared with conventional meta-analysis methods, taxonomic meta-analyses can include the results of natural experiments, policy initiatives, program implementation efforts and highly controlled experiments (as examples) regardless of the design of the report being analyzed as long as the intended outcome is the same. It also characterizes the domain of interventions that have been studied. Conclusion: Taxonomic meta-analysis can be a powerful tool for summarizing the evidence that exists and for generating hypotheses that are worthy of more rigorous testing.